A Machine Learning Application for Latency Prediction in Operational 4G Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measuring performance on Internet is always challenging. When it comes to the mobile networks, the variety of technology characteristics coupled with the opaque network configuration make the performance evaluation even a more difficult task. Latency is one of the aspects having the largest impact on the performance and on the end users’ Quality of Experience. In this paper, we present a machine learning approach that, exploiting real mobile network data on the end user, try to predict the latency in a real operational network. We consider a large-scale dataset with more than 238 million latency measurements coming from 3 different commercial mobile operators. The presented methodology flattens the RTT values into several bins, turning the latency prediction problem to a multi-label classification problem. Then, three well-known supervised algorithms are exploited to predict the latency. The obtained results highlight the importance of representative dataset from operational network. It calls for further improvements on the algorithm selection, tuning, and their predictive capabilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it